This document discusses lessons learned from European data management projects that could be applied more broadly. It summarizes Cineca's role as a supercomputing center in Italy supporting research through HPC and large-scale data analysis. European projects like EuHIT and EUDAT aim to integrate cross-border research infrastructure and offer common data services. While standards differ across disciplines, cloud infrastructure can help lower barriers to FAIR data practices. Communities need flexibility in applying principles progressively rather than all at once. Accessibility remains a priority, but interoperability and reusability of data need more focus.

1. Data management experiences in
the European projects context:
which lessons for us?which lessons for us?
Claudio Cacciari, c.cacciari@cineca.it (Cineca)
FAIR data management
RDA National Event in Italy
Florence, Italy, 14-15 November 2016
This work is licensed under the Creative Commons Attribution 4.0 International License.

2. Cineca consortium
• Cineca is a not for profit Consortium of Universities.
Founded in 1969, today is made up of 70 Universities,
six national Reseach Institutions, and the Italian
Ministry of Education, Universities and
Research (MIUR).
• It is the most powerful supercomputing center in Italy• It is the most powerful supercomputing center in Italy
devoted to scientific and industrial research, and one
of the most important worldwide. Cineca supports
the international community of scientific research with
HPC and Big Data analysis, it develops information
systems for universities administation offices, for the
MIUR, and for companies, health care, and public
administration.
http://www.cineca.it

3. Cineca’s users
http://www.hpc.cineca.it/content/statistics-cineca-hardware-utilization-september-2016

4. Cineca’s storage resources
May 2015
Online storage will reach 20 Pbytes in 2017

5. Cineca’s scenario
European projects
Projects
FAIR
principles
Italian projects
Services &
resourcesCloud
HPC
Big Data
analytics
Long term
archiving
Tape
library
High
performance
data transfer

6. Cloud
HPC
plain fs
Long
term
archive

7. Lowering the barrier
• In many cases the tools that support FAIR
principles are too complex for the scientific
researchers
• The Cloud infrastructure allows to lower the• The Cloud infrastructure allows to lower the
barrier deploying more user-friendly tools
close to the data and sharing the effort of
their implementation/maintaining.

8. EUHIT Portal

9. EuHIT
EuHIT is a consortium that aims at integrating cutting-edge European
facilities for turbulence research across national boundaries.

10. Data repository

11. EUDAT
A truly pan-European Infrastructure
EUDAT offers common data services,
supporting multiple research communities as
well as individuals, through a geographically
distributed, resilient network of 35 European
organisations
Our vision is to enable European
researchers and practitionersresearchers and practitioners
from any research discipline to
preserve, find, access, and
process data in a trusted
environment, as part of a
Collaborative Data
Infrastructure

12. EUDAT Service Suite
http://www.eudat.eu/services

13. Conclusions 1
• There is an immense heterogeneity about data
and metadata specifications among the different
disciplines and often within each discipline too.
• If the communities of a scientific domain are not• If the communities of a scientific domain are not
able to, at least partially, converge towards
common standards, the data centers offering
Data and Computing services/resources cannot
fill the gap on their behalf.

14. Conclusions 2
• The data centers should offer services and
enforce policies which support FAIR principles
in a flexible way.
• Some scientific communities can/want to• Some scientific communities can/want to
comply with some reccomendations, but not
others. The data services should allow the
community to improve its compliancy
progressively.

15. Conclusions 3
• We see interest in making the data accessible
and findable.
• Not much to make them interoperable and re-
usable.usable.

16. Thanks !Thanks !